Dynamic Factor Copula Models with Estimated Cluster Assignments

Dong Hwan Oh and Andrew Patton
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Dong Hwan Oh: https://www.federalreserve.gov/econres/dong-hwan-oh.htm

No 2021-029r1, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

Keywords: high-dimensional models; risk management; multivariate density forecasting (search for similar items in EconPapers)
JEL-codes: C32 C38 C58 (search for similar items in EconPapers)
Pages: 59 p.
Date: 2021-04-30, Revised 2022-05-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2021-29

DOI: 10.17016/FEDS.2021.029r1

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